P1208: TRANSCRIPTOMIC PROFILING OF B CELL STATES AND MICROENVIROMENTAL ECOSYSTEMS REFINES THE PROGNOSTIC CLASSIFICATION OF DIFFUSE LARGE B CELL LYMPHOMA
Authors: Robel Papotti, Filippo Vit, Federico Pozzo, Tamara Bittolo, Stefania Bettelli, Samantha Pozzi, Antonino Maiorana, Elisa Forti, Arianna DI Napoli, Maria Christina Cox, Tamar Tadmor, Giovanna Mansueto, Pellegrino Musto, Leonardo Flenghi, Martina Quintini, Vittoria Lalinga, Sara Galimberti, Valentina Donati, Michele Spina, Alberto Zamò, Andreas Rosenwald, Riccardo Bomben, Stefano Sacchi, Valter Gattei
Journal: HemaSphere (2023)

Topic: 20. Lymphoma Biology & Translational Research Background: Diffuse Large B-cell Lymphoma (DLBCL) is the most frequent aggressive lymphoma, originating either from the germinal-center (GCB) or post-germinal cells (ABC). Currently, response rate to first-line therapy (R-CHOP) is approximately 60%. Recently, EcoTyper, an integrative machine-learning approach based on transcriptome data, has been proposed for dissecting cellular heterogeneity in DLBCL (Steen et al., Cancer Cell, 2021). This tool, able to identify cell states and tumor microenvironment (TME) ecosystems, proposes prognostic associations with clinical relevance beyond the canonical DLBCL classifications. Aims: By taking advantage of a well-characterized homogeneously treated cohort of real-world DLBCL, the aim is to validate and expand the clinical impact of the diverse cellular ecosystems, as investigated by the EcoTyper approach. Methods: The project comprises 204 DLBCL FFPE samples from patients homogeneously treated with R-CHOP. Cell of origin (COO) classification identified 41% GCB, and 59% ABC cases (Hans’ algorithm), or 47% GCB, 35% ABC, and 18% unclassified cases (Lymph2Cx assay; concordance R=0.719). RNAseq was performed on a NovaSeq 6000 platform (Illumina). The EcoTyper algorithm was applied to RNAseq data (n=186) to retrieve the different cell states and the interactions in TME stereotyped ecosystems, as reported. Results: EcoTyper detected 5 malignant B-cells states (S1-S5) corresponding to 5 different transcriptional programs (Fig. A). Interestingly, the state-associated progression free survival (PFS) curves showed distinct outcomes with the most emphatic distance between S1 and S5 (p=0.0012; Fig. B). When stratified for COO, the S1 and the S5 B-cell states tend to be mutually exclusive, with S1 and S5 respectively displaying GCB-like or ABC-like signatures (p<0.0001). By assembling the states of B-cells and TME cells into communities, maximizing co-association patterns, the EcoTyper algorithm generated 9 different Lymphoma Ecotypes (LEs) subgroups (Fig. C). Again, PFS analysis displayed significant differences between LEs (p=0.0028) with the maximum distance between LE1-2 and LE9, the latter showing the best outcome (Fig. D). Accordingly, ABC cases were more frequently associated with LE1 and LE2, two ecotypes related with the highest B-cell infiltration virtually in the absence of microenvironmental interactions, whereas GCB cases were linked to Ecotypes LE7, LE8 and LE9 showing the strongest TME activity (p<0.0001). As expected, both COO and R-IPI maintained their clinical impact, with the ABC and R-IPI poor groups having the worst outcome. The B-cell states or the LE subgroups, respectively split into three (S1, S2-S3-S4, S5) or four (LE1-2-3, LE4-5-6, LE7-8, LE9) categories, when introduced into two different multivariate models (n=138) along with R-IPI and COO, remained independent PFS predictors (p<0.05) of poor outcome along with R-IPI (p<0.005). Interestingly, the B-cell states further stratified R-IPI poor patients, the S1 B-cell state identifying a subgroup with an outcome comparable to R-IPI very good/good cases, and the S5 B-cell state marking the cases with the worst outcome (Fig. E-F). Summary/Conclusion: Here we confirm that trascriptome-based approaches coupled with new deconvolution methods could be informative of the outcome of DLBCL patients homogeneously treated with the R-CHOP regimen. In this context, information regarding the B-cell states and the TME cells transcriptional programs, could contribute to refine the prognostic risk stratification of DLBCL patients by providing a better selection of cases potentially eligible for different therapy approaches.Keywords: Gene expression profile, Prognostic factor, Diffuse large B cell lymphoma